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Posted on by Rudeus Greyrat

En este artículo, vamos a discutir la función Groupby en PySpark usando Python.

Vamos a crear el marco de datos para la demostración:

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# display
dataframe.show()

Producción:

En PySpark,   groupBy() se usa para recopilar datos idénticos en grupos en PySpark DataFrame y realizar funciones agregadas en los datos agrupados.

La operación de agregación incluye:

  • count(): esto devolverá el recuento de filas para cada grupo.

dataframe.groupBy(‘column_name_group’).count()

  • mean(): Esto devolverá la media de los valores para cada grupo.

dataframe.groupBy(‘column_name_group’).mean(‘column_name’)

  • max(): Esto devolverá el máximo de valores para cada grupo.

dataframe.groupBy(‘column_name_group’).max(‘column_name’)

  • min(): Esto devolverá el mínimo de valores para cada grupo.

dataframe.groupBy(‘column_name_group’).min(‘column_name’)

  • sum(): Esto devolverá los valores totales para cada grupo.

dataframe.groupBy(‘column_name_group’).sum(‘column_name’)

  • avg(): Esto devolverá el promedio de valores para cada grupo.

dataframe.groupBy(‘column_name_group’).avg(‘column_name’).show()

Tenemos que usar cualquiera de las funciones con groupby mientras usamos el método

Sintaxis : dataframe.groupBy(‘column_name_group’).aggregate_operation(‘column_name’)

Ejemplo 1: Groupby con sum()

Groupby con DEPT junto con FEE con sum().

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# Groupby with DEPT along FEE with sum()
dataframe.groupBy('DEPT').sum('FEE').show()

Producción:

Ejemplo 2: Groupby con min()

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# Groupby with DEPT along FEE with min()
dataframe.groupBy('DEPT').min('FEE').show()

Producción:

Ejemplo 3: Groupby con max()

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# Groupby with DEPT along FEE with max()
dataframe.groupBy('DEPT').max('FEE').show()

Producción:

Ejemplo 4: Groupby con avg()

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# Groupby with DEPT along FEE with avg()
dataframe.groupBy('DEPT').avg('FEE').show()

Producción:

Ejemplo 5: Groupby con count()

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# Groupby with DEPT  with count()
dataframe.groupBy('DEPT').count().show()

Producción:

Ejemplo 6: Groupby con mean()

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# Groupby with DEPT  with mean()
dataframe.groupBy('DEPT').mean('FEE').show()

Producción:

Aplicando groupby() en múltiples columnas

Aquí vamos a usar groupby() en varias columnas.

Sintaxis: dataframe.groupBy(‘column_name_group1′,’column_name_group2′,…………,’column_name_group n’).aggregate_operation(‘column_name’)

Ejemplo 1: Groupby con funciones mean() con DEPT y NAME

Python3

# importing module
import pyspark
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# Groupby with DEPT and NAME with mean()
dataframe.groupBy('DEPT', 'NAME').mean('FEE').show()

Producción:

También podemos agrupar y agregar en varias columnas a la vez usando la siguiente sintaxis:

dataframe.groupBy(“group_column”).agg( max(“column_name”),sum(“column_name”),min(“column_name”),mean(“column_name”),count(“column_name”).show()

Tenemos que importar estas funciones agregadas desde el módulo sql.functions.

Ejemplo:

Python3

# importing module
import pyspark
  
# import sum, min,avg,count,mean and max functions
from pyspark.sql.functions import sum, max, min, avg, count, mean
  
# importing sparksession from pyspark.sql module
from pyspark.sql import SparkSession
  
# creating sparksession and giving an app name
spark = SparkSession.builder.appName('sparkdf').getOrCreate()
  
# list  of student  data
data = [["1", "sravan", "IT", 45000],
        ["2", "ojaswi", "CS", 85000],
        ["3", "rohith", "CS", 41000],
        ["4", "sridevi", "IT", 56000],
        ["5", "bobby", "ECE", 45000],
        ["6", "gayatri", "ECE", 49000],
        ["7", "gnanesh", "CS", 45000],
        ["8", "bhanu", "Mech", 21000]
        ]
  
# specify column names
columns = ['ID', 'NAME', 'DEPT', 'FEE']
  
# creating a dataframe from the lists of data
dataframe = spark.createDataFrame(data, columns)
  
# Groupby with DEPT  with sum() , min() , max()
dataframe.groupBy("DEPT").agg(max("FEE"), sum("FEE"),
                              min("FEE"), mean("FEE"), 
                              count("FEE")).show()

Producción:

Publicación traducida automáticamente

Artículo escrito por sravankumar8128 y traducido por Barcelona Geeks. The original can be accessed here. Licence: CCBY-SA

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